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GraphQL is a data query and manipulation language that allows specifying what data is to be retrieved ("declarative data fetching") or modified. A GraphQL server can process a client query using data from separate sources and present the results in a unified graph. [2] The language is not tied to any specific database or storage engine.
This query would return the city of residence of each person in the graph with residential information, and, if an EU national, which country they come from. Queries are therefore able to first project a sub-graph of the graph input into the query, and then extract the data values associated with that subgraph.
GraphQL: an open-source data query and manipulation language for APIs. Dgraph implements modified GraphQL language called DQL (formerly GraphQL+-) Gremlin: a graph programming language that is a part of Apache TinkerPop open-source project [49] SPARQL: a query language for RDF databases that can retrieve and manipulate data stored in RDF format
You aren't gonna need it" [1] [2] (YAGNI) [3] is a principle which arose from extreme programming (XP) that states a programmer should not add functionality until deemed necessary. [4] Other forms of the phrase include "You aren't going to need it" (YAGTNI) [ 5 ] [ 6 ] and "You ain't gonna need it".
[citation needed] Real computers constructed so far can be functionally analyzed like a single-tape Turing machine (which uses a "tape" for memory); thus the associated mathematics can apply by abstracting their operation far enough. However, real computers have limited physical resources, so they are only linear bounded automaton complete.
In other databases, alternatives to express the same query (other queries that return the same results) can be tried. Some query tools can generate embedded hints in the query, for use by the optimizer. Some databases - like Oracle - provide a plan table for query tuning. This plan table will return the cost and time for executing a query.
The rate of a code is inversely related to the query complexity, but the exact shape of this tradeoff is a major open problem. [8] [9] It is known that there are no LDCs that query the codeword in only one position, and that the optimal codeword size for query complexity 2 is exponential in the size of the original message. [8]
The expression trees are handed over to LINQ Providers, which are data source-specific implementations that adapt the LINQ queries to be used with the data source. If they choose so, the LINQ Providers analyze the expression trees contained in a query in order to generate essential pieces needed for the execution of a query.